wolf_df <- read.csv(here("data","greywolf_GBIF.csv")) %>%
clean_names() %>%
select(species,
country_code,
locality,
state_province,
decimal_latitude,
decimal_longitude,
event_date,
day,
month,
year,
identified_by)
wolf_df$decimal_latitude <- as.numeric(wolf_df$decimal_latitude)
wolf_df$decimal_longitude <- as.numeric(wolf_df$decimal_longitude)
# wolf_df <- wolf_df %>%
#drop_na()
wolf_spatial = SpatialPoints(cbind(wolf_df$decimal_longitude, wolf_df$decimal_latitude), proj4string=CRS("+proj=longlat +datum=WGS84"))
st_crs(wolf_spatial)
## Coordinate Reference System:
## User input: +proj=longlat +datum=WGS84 +no_defs
## wkt:
## GEOGCRS["unknown",
## DATUM["World Geodetic System 1984",
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]],
## ID["EPSG",6326]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8901]],
## CS[ellipsoidal,2],
## AXIS["longitude",east,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433,
## ID["EPSG",9122]]],
## AXIS["latitude",north,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433,
## ID["EPSG",9122]]]]
# then converting it to an sp and data frame
wolf_spatial_transform = spTransform(wolf_spatial, CRS("+proj=longlat +datum=WGS84")) %>% as.data.frame()
# then cbinding it to the original wolf_df and removing our old "decimal_latitude" & "decimal_longitude" and renaming our new UTM coordinates
wolf_spatial_latlong<- cbind(wolf_df,wolf_spatial_transform) %>%
rename(lat = coords.x1, long = coords.x2) %>%
select(-decimal_latitude, -decimal_longitude)
## converting the lat long to crs 4326
wolf_spatial_cord <- st_as_sf(wolf_spatial_latlong, coords = c("lat", "long"), crs = 4326)
# st_crs(wolf_spatial_cord)
ggplot() +
geom_sf(data = wolf_spatial_cord)
### interactive map
# world <- ne_countries(scale = "medium", returnclass = "sf")
# class(world)
# st_crs(world)
# world <- st_transform(world, st_crs(wolf_spatial_cord))
# ggplot(data = world) +
geom_sf(data = wolf_spatial_cord)
## [[1]]
## mapping:
## geom_sf: na.rm = FALSE
## stat_sf: na.rm = FALSE
## position_identity
##
## [[2]]
## <ggproto object: Class CoordSf, CoordCartesian, Coord, gg>
## aspect: function
## backtransform_range: function
## clip: on
## crs: NULL
## datum: crs
## default: TRUE
## distance: function
## expand: TRUE
## fixup_graticule_labels: function
## is_free: function
## is_linear: function
## label_axes: list
## label_graticule:
## labels: function
## limits: list
## modify_scales: function
## ndiscr: 100
## range: function
## render_axis_h: function
## render_axis_v: function
## render_bg: function
## render_fg: function
## setup_data: function
## setup_layout: function
## setup_panel_guides: function
## setup_panel_params: function
## setup_params: function
## train_panel_guides: function
## transform: function
## super: <ggproto object: Class CoordSf, CoordCartesian, Coord, gg>
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(wolf_spatial_cord) +
tm_dots()
# wolf_df$country_code <- as.character(wolf_df$country_code)
wolf_sum <- wolf_df %>%
group_by(country_code) %>%
summarize(n()) %>%
rename(wolf = 'n()')
## `summarise()` ungrouping output (override with `.groups` argument)
wolf_sum <- wolf_sum %>%
mutate(country_code = fct_reorder(country_code, wolf))
ggplot(data = wolf_sum,
aes(x = country_code,
y = wolf)) +
geom_bar(stat="identity", fill="#D81313", alpha=.6, width=.4) +
coord_flip() +
theme_minimal() +
labs(y = 'Count of Grey Wolves (Canis lupus) from 1700 to 2021',
x = "Country Code")
wolf_count_year_country <- wolf_df %>%
group_by(year, country_code) %>%
summarize(n()) %>%
rename(wolf = 'n()') %>%
drop_na()
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = wolf_count_year_country,
aes(x = year,
y = wolf,
group = country_code)) +
geom_line(alpha=.6, width=3, aes(color = country_code)) +
theme_minimal() +
labs(y = 'Count of Grey Wolves (Canis lupus)',
x = "Year (1700 - 2021)") +
scale_color_brewer(palette="Dark2")
## Warning: Ignoring unknown parameters: width